Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model

Abstract

The success of deep brain stimulation (DBS) is dependent on the accurate placement of electrodes in the operating room (OR). However, due to intraoperative brain shift, the accuracy of pre-operative scans and pre-surgical planning are often degraded. To compensate for brain shift, we created a finite element bio-mechanical brain model that updates preoperative images by assimilating intraoperative sparse data from the brain surface or deep brain targets. Additionally, we constructed an artificial neural network (ANN) that leveraged a large number of ventricle nodal displacements to estimate brain shift. The machine learning method showed potential in incorporating ventricle sparse data to accurately compute shift at the brain surface. Thus, in this paper, we propose using this machine learning model to estimate brain atrophy at deep brain targets such as the anterior commissure (AC) and the posterior commissure (PC). The ANN consists of an input layer with nine hand-engineered features, such as the distance between the deep brain target and the ventricle node, two hidden layers and an output layer. This model was trained using eight patient cases and tested on two patient cases.